# agent-memory

Persistent memory layer for AI coding agents — keeps Claude Code, Codex, Cursor, and any MCP agent in context across sessions

agentmemory is an open-source MCP server that gives AI coding agents persistent, cross-session memory. Built on hybrid vector-graph search, it achieves 95.2% recall on the LongMemEval-S benchmark while using up to 92% fewer context tokens than naive context injection. Works out of the box with Claude Code, Codex, Cursor, Windsurf, Cline, OpenCode, Kilo Code, Hermes, and any MCP client through 51 MCP tools plus 12 hooks and 4 skills.

Context engineering platform for AI agents with temporal knowledge graphs

Zep is a context engineering platform that assembles relationship-aware context for AI agents from conversations, business data, documents, and events. It maintains a temporal knowledge graph that automatically extracts entities and relationships, tracking how context evolves over time. Zep delivers formatted context blocks optimized for LLMs with sub-200ms latency, integrating with LangChain, LlamaIndex, AutoGen, and Google ADK through Python, TypeScript, and Go SDKs.

Agent memory system that learns, not just remembers

Hindsight is an agent memory system that enables AI agents to learn from experience rather than just store conversations. It organizes memories into three biomimetic categories: World knowledge for facts, Experiences for agent events, and Mental Models for learned understanding. The system provides retain, recall, and reflect operations backed by a temporal knowledge graph with parallel retrieval strategies including semantic, keyword, graph traversal, and temporal search.

Knowledge graph memory engine for AI agents

Cognee is an open-source knowledge engine that builds persistent memory for AI agents by combining vector search with graph databases. It ingests data from 38+ source formats, structures information into a knowledge graph with embeddings, and enables semantic and relational queries through its ECL pipeline. Its cognitive science-inspired architecture provides superior cross-document entity identification compared to traditional RAG approaches.